code: def main():
      args = parse_args()
  
      cfg = Config.fromfile(args.config)
      if args.cfg_options is not None:
          cfg.merge_from_dict(args.cfg_options)
      # import modules from string list.
      if cfg.get("custom_imports", None):
          from mmcv.utils import import_modules_from_strings
  
          import_modules_from_strings(**cfg["custom_imports"])
      # set cudnn_benchmark
      if cfg.get("cudnn_benchmark", False):
          torch.backends.cudnn.benchmark = True
  
      # work_dir is determined in this priority: CLI > segment in file > filename
      if args.work_dir is not None:
          # update configs according to CLI args if args.work_dir is not None
          cfg.work_dir = args.work_dir
      elif cfg.get("work_dir", None) is None:
          # use config filename as default work_dir if cfg.work_dir is None
          cfg.work_dir = osp.join(
              "./work_dirs", osp.splitext(osp.basename(args.config))[0]
          )
      if args.resume_from is not None:
          cfg.resume_from = args.resume_from
      if args.gpu_ids is not None:
          cfg.gpu_ids = args.gpu_ids
      else:
          cfg.gpu_ids = (
              range(1) if args.gpus is None else range(args.gpus)
          )
  
      # init distributed env first, since logger depends on the dist info.
      if args.launcher == "none":
          distributed = False
      else:
          distributed = True
          init_dist(args.launcher, **cfg.dist_params)
          # re-set gpu_ids with distributed training mode
          _, world_size = get_dist_info()
          cfg.gpu_ids = range(world_size)
  
      # create work_dir
      mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))
      # dump config
      cfg.dump(osp.join(cfg.work_dir, osp.basename(args.config)))
      # init the logger before other steps
      timestamp = time.strftime("%Y%m%d_%H%M%S", time.localtime())
      log_file = osp.join(cfg.work_dir, f"{timestamp}.log")
      logger = get_root_logger(
          log_file=log_file, log_level=cfg.log_level
      )
  
      # init the meta dict to record some important information such as
      # environment info and seed, which will be logged
      meta = dict()
      # log env info
      env_info_dict = collect_env()
      env_info = "\n".join(
          [(f"{k}: {v}") for k, v in env_info_dict.items()]
      )
      dash_line = "-" * 60 + "\n"
      logger.info(
          "Environment info:\n"
          + dash_line
          + env_info
          + "\n"
          + dash_line
      )
      meta["env_info"] = env_info
      meta["config"] = cfg.pretty_text
      # log some basic info
      logger.info(f"Distributed training: {distributed}")
      logger.info(f"Config:\n{cfg.pretty_text}")
  
      # set random seeds
      if args.seed is not None:
          logger.info(
              f"Set random seed to {args.seed}, "
              f"deterministic: {args.deterministic}"
          )
          set_random_seed(args.seed, deterministic=args.deterministic)
      cfg.seed = args.seed
      meta["seed"] = args.seed
      meta["exp_name"] = osp.basename(args.config)
  
      model = build_detector(
          cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg
      )
  
      datasets = [build_dataset(cfg.data.train)]
      # print('ds count', len(datasets))
      if len(cfg.workflow) == 2:
          val_dataset = copy.deepcopy(cfg.data.val)
          val_dataset.pipeline = cfg.data.train.pipeline
          datasets.append(build_dataset(val_dataset))
      if cfg.checkpoint_config is not None:
          # save mmdet version, config file content and class names in
          # checkpoints as meta data
          cfg.checkpoint_config.meta = dict(
              mmdet_version=__version__ + get_git_hash()[:7],
              CLASSES=datasets[0].CLASSES,
          )
      # add an attribute for visualization convenience
      model.CLASSES = datasets[0].CLASSES
      train_detector(
          model,
          datasets,
          cfg,
          distributed=distributed,
          validate=(not args.no_validate),
          timestamp=timestamp,
          meta=meta,
      )
  
file_path: insightface/detection/scrfd/tools/train.py
imports: import argparse
  import copy
  import os
  import os.path
  import time
  import warnings
  import mmcv
  import torch
  from mmcv import Config, DictAction
  from mmcv.runner import get_dist_info, init_dist
  from mmcv.utils import get_git_hash
  from mmdet import __version__
  from mmdet.apis import set_random_seed, train_detector
  from mmdet.datasets import build_dataset
  from mmdet.models import build_detector
  from mmdet.utils import collect_env, get_root_logger
label_desc: Use image node_id 2 for calling a global function with example usage: main() without return types